Beyond data that can be represented in machine readable tabular form and, of course, machine readable text documents, many other forms of media are transitioning to machine readable digital form. For example, visual data such as images and video are increasingly being produced in digital form or converted to digital form. Large collections and catalogues of these media objects need to be organized, similarly to structured text data, but using categorization technology enhanced with new technologies that allow for convenient categorization based on visual and audio content of the media. Such collections of media are managed using multimedia databases where the data that are stored are combinations of numerical, textual, auditory and visual data.
Video is a special, peculiar type of data object in the sense that there is a notion of time associated with this data. These types of data are referred to as streamed information, streamed multimedia data or temporal media. When transporting this data from one location to some other location for viewing purposes, it is important that the data arrives in the right order and at the right time. In other words, if frame n of a video is displayed at time t, frame n+1 has to be at the viewing location at time t plus 1/30th of a second. Of course, if the media are moved or transported for other purposes, there is no such requirement.
Similarly to text documents, which can be segmented into sections, paragraphs and sentences, temporal media data can be divided up into smaller more or less meaningful time-continuous chunks. For video data, these chunks are often referred to as scenes, segments and shots, where a shot is the continuous depiction of space by a single camera between the time the camera is switched on and switched off, i.e., it is an image of continuous space-time. In this disclosure, we refer to these temporal, time-continuous (but not necessarily space-continuous) chunks of media as media items. These media items include image and video, with associated audio or text and, in general, information stream items composed of disparate sources of information. Examples of media items are commercial segments (or groups) broadcast at regular time intervals on almost every TV channel; a single commercial is another example of a media item or video segment.
Multimedia databases may contain collections of such temporal media items in addition to non-streamed media objects such as still images and text documents. Associated with the media items may be global textual or parametric data, such as the name of the director of the video/music (audio) or the date of recording. Categorization of these media items into classes can be accomplished through supervised and unsupervised clustering and decision tree generation based on the text and, possibly, parametric data.
Multimedia collections may also be categorized based on data content, such as the amount of green or red in images or video and sound frequency components of audio segments. The media item collections have to be then preprocessed and the results have to be somehow categorized based on the visual properties. Categorizing media items based on semantic content, the actual meaning (subjects and objects) of the media items, on the other hand, is a difficult issue. For video, speech may be categorized or recognized to some extent, but beyond that, the situation is much more complicated because of the rudimentary state of the art in machine-interpretation of visual data.
Determining whether a given media item is equal to (a piece of) one of, or is similar to (a piece of) one of, a plurality of temporal media items; or, determining whether it is equal or similar to a media item or equal or similar to a sub segment in a media item collection is another important multimedia categorization problem. A variant here is the issue of determining if a given temporal input media item contains a segment which is equal or similar to one of a plurality of temporal media stream segments or determining if the input stream contains a segment which is equal or similar to a media item in a multimedia database. To achieve this one needs to somehow compare a temporal media item to a plurality of temporal media items or databases of such items. This problem arises when certain media items need to be selected or deselected in a given temporal media item or in a plurality of temporal media items. An example here is the problem of deselecting or suppressing repetitive media items in a television broadcast program. Such repetitive media items can be commercials or commercial segments or groups which are suppressed either by muting the sound channel or by both muting the sound channel and blanking the visual channel.
To develop a procedure for identifying media items as belonging to particular classes or categories, (or for any classification or pattern recognition task, for that matter) supervised learning technology can be based on decision trees, on logical rules, or on other mathematical techniques such as linear discriminant methods (including perceptrons, support vector machines, and related variants), nearest neighbor methods, Bayesian inference, etc. We can generically refer to the output of such supervised learning systems as classifiers.
Supervised learning technology requires a training set consisting of labeled data, that is, representations of previously categorized media segments, to enable a computer to induce patterns that allow it to categorize hitherto unseen media segments. Generally, there is also a test set, also consisting of labeled data, that is used to evaluate whatever specific categorization procedure is developed. In academic exercises, the test set is usually disjoint from the training set to compensate for the phenomenon of overfitting. In practice, it may be difficult to get large amounts of labeled data of high quality. If the labeled data set is small, the only way to get any useful results at all may be to use all the available data in both the training set and the test set.
To apply standard approaches to supervised learning, the media segments in both the training set and the test set must be represented in terms of numbers derived from counting occurrences of features. The relationship between features extracted for the purposes of supervised learning and the content of a media segment has an important impact on the success of the enterprise, so it has to be addressed, but it is not part of supervised learning per se.
From these feature vectors, the computer induces classifiers based on patterns or properties that characterize when a media segment belongs to a particular category. The term “pattern” is meant to be very general. These patterns or properties may be presented as rules, which may sometimes be easily understood by a human being, or in other, less accessible formats, such as a weight vector and threshold used to partition a vector space with a hyperplane. Exactly what constitutes a pattern or property in a classifier depends on the particular machine learning technology employed. To use a classifier to categorize incoming hitherto unseen media segments, the newly arriving data must not only be put into a format corresponding to the original format of the training data, but it must then undergo a further transformation based on the list of features extracted from the training data in the training phase, so that it finally possesses a representation as a feature vector that permits the presence or absence of the relevant patterns or properties to be determined.
The assignment of more than one category to an item is called multiple categorization. Some supervised learning techniques (for example, a few, but not all, approaches using decision trees) do not support multiple categorization. They make the assumption that each item categorized will belong to at most one category, which may not be adequate in some applications. Some supervised learning systems may return a ranked list of possibilities instead of a single category, but this is still slightly deficient for some applications, because such a system might assign categories even to items that should be placed in no category. What are usually most useful are those supervised learning methods that give realistic confidence levels with each assigned category.
The idea behind text feature selection is that the occurrence of the selected features in text associated with an unclassified data item will be a useful ingredient for the development of an automated classification system designed to assign one or more categories to the data item. For text data, the first processing step that must be done is tokenization, i.e., the segmentation of a string of characters into a string of words or tokens. However, the representation of an item of text data as a string of arbitrary words, with all of the meaningful linguistic structures it implicitly contains, is often simply too complicated and rich for a computer to handle. Even if one does no parsing of the text, there may well be too many potential features, in which case some distillation is needed. Luckily, single words themselves have been seen to comprise an adequate set of features for many supervised learning problems. Sometimes it is useful to identify the part of speech of each word, thus distinguishing between an instance of the verb walk and the noun walk. (This is called part-of-speech tagging.) This only scratches the surface. Modern techniques of computational linguistics permit the identification of complex features in text, but with rising complexity comes vast numbers of features. At any rate, after the training set is prepared, and after the text associated with it is identified, a list of those text features deemed particularly relevant to the particular classification task at hand is automatically extracted. Call the features in this list the extracted text features, and call the process of building the list text feature extraction. There is an issue in regard to whether a single list of features, called in this setting a global dictionary, is created or whether there is a separate list for each category, called in this context local dictionaries. The resolution of this issue can depend on the details of the supervised learning technique employed, but, in applications related to text, local dictionaries generally give better performance. There are a variety of criteria for judging relevance during feature extraction. A simple one is to use absolute or normalized frequency to compile a list of a fixed number n of the most frequent features for each category, taking into account the fact that small categories may be so underpopulated that the total number of features in them may be less than n. More sophisticated techniques for judging relevance involve the use of information-theoretic measures such as entropy or the use of statistical methods such as principal component analysis.
After text feature extraction, a new vector representation of each text item associated with the training data is then extracted in terms of how frequently each selected feature occurs in that item. The vector representation may be binary, simply indicating the presence or absence of each feature, or it may be numeric in which each numeric value is derived from a count of the number of occurrences of each feature.
A large body of prior art of video processing for video identification, detection, categorization, and classification is concerned with the detection of commercials in a video stream, i.e., the media item is a commercial or a sequence of commercials. This is not a categorization problem per se, but rather a detection problem. The detection of one class (or category) of interest, though, is in itself a categorization problem, where the categories are “category-of-interest” and “unknown.”
Many methods rely on the fact that commercials are often surrounded by blank frames, changes in audio/brightness level, simple representations of intermediate frames and more global dynamic properties that typically hold for commercials. An example of a method and apparatus for detection and identification of portions of temporal video streams containing commercials is described in U.S. Pat. No. 5,151,788 to Blum. Here, a blank frame is detected in the video stream and the video stream is tested for “activity” (properties such as sound level, brightness level and average shot length). U.S. Pat. No. 5,696,866 to Iggulden et al. extend the idea to detecting a “flat” frame. In addition to a frame being flat at the beginning and end of a commercial, they include that the frame has to be silent. Additional features, such as changes in the audio power or amplitude and changes in brightness of the luminance signal between program and commercial segments, of the video signal are used in U.S. Pat. No. 5,343,251 to Nafeh.
Many techniques for detecting commercials, reduce commercials to a small set of representative frames, or key frames, and then use image matching schemes to match the key frames. Here, each particular commercial has some representation, instead of using generic attributes above that describe the category of commercials. For example, U.S. Pat. No. 5,708,477 to S. J. Forbes et al. uses the notion of a list of abbreviated frames for representing commercial video segments. An abbreviated frame is an array of digital values representing the average intensities of the pixels in a particular portion of the video frame. Upon detection of a scene change in the live video stream, computed and stored abbreviated frames are matched and commercial is detected and classified (if present in memory). A technique that uses more sophisticated frame representations is presented in reference:                J. M. Sanchez, X. Binefa, J. Vitria, and P. Radeva, Local color analysis for scene break detection applied to TV commercial recognition, Third International Conference, Visual'99, Amsterdam, June 1999, pp. 237–244.        
(This reference is incorporated herein in its entirety.) Each commercial in the database is represented by a number of color histograms, or color frequency vectors, for a representative frame for each shot in the commercial. The shot boundaries of a commercial are detected by some shot boundary detection algorithm (finding scene breaks). Commercials are detected in a live video stream by comparing all the color histograms of all the commercials to the color histograms representing a shot in video stream. No temporal information is incorporated in the representation of the commercial.
All this prior art falls in the realm of detection of video copies. The use of image feature histograms, where the images are particular video frames, like shot boundaries, have been popularized in the area of image recognition, and, later on in the area of image search. Color histograms (color frequency distributions) are the most widely used, in particular, the Red Green Blue (RGB) and the Hue Saturation and Intensity (HSI). Other color spaces that could be used are those defined by the CIE (Commission Internationale de l'Eclairage—the International Committee for Illumination). These spaces are CIE L*u*v* hue angle and saturation and CIE L*a*v* hue angle and saturation. Ratios of color components such as the red response divided by the green response (after appropriate gamma correction) also yield intensity independent color measures. Another popular method is to divide each response by the average response across all spectral bands, such as Rn=R/(R+G+B), to produce a set of fractional color components (which sum to one).
A particular instance of image database search, is image classification, or image content recognition. In an image classification problem, typically, the number of classes is smaller than the number of images in an image database. An example of image classification is found in:                R. Bolle, J. Connell, G. Taubin, N. Haas, R. Mohan, “VeggieVision: A produce recognition system,” in Proc. Third IEEE Workshop on Applications of Computer Vision, pp. 244–251, December 1996.        
This reference is incorporated herein in its entirety. Color histograms are used in this work, but the use of color frequency distributions is extended to the use of histograms to represent other features that are used for image/object classification. Histograms are a compact representation of a reference image that do not depend on the location or orientation of the object in the image, or, at least, depend only a little because of quantization effects. For example, visual texture is a feature used in “VeggieVision” to Bolle et al. As opposed to color, texture is a visual feature that is much more difficult to describe and to capture computationally. It is also a feature that cannot be attributed to a single pixel but rather is attributed to a patch of image data. The texture of an image patch is a description of the spatial brightness variation in that patch. This can be a repetitive pattern of primitives (texels), or, can be more random, i.e., structural textures and statistical textures. Computational texture measures are either region-based or edge-based, trying to capture structural textures and statistical textures, respectively. In “VeggieVision” to Bolle et al., a texture representation of an image, image class, or image category, then, is a one-dimensional histogram of local texture feature values. Shape can also be represented in terms of frequency distribution. The information available to work with is the two-dimensional boundary of (say) a segmented image. Boundary shape is a feature of multiple boundary pixels and is expressed by a local computational feature, for example, curvature. Local curvature is estimated by fitting a circle at each point of the boundary. After smoothing, this boundary shape feature is quantized and a histogram is computed. Instead of over an area, such as for color histograms, these histograms are computed from a collection of image pixels that form the boundary of the object image. Finally, size of image segments is another feature of the images that is important in “VeggieVision” to Bolle et al. A method that computes area from many collections of three boundary points is proposed. Three points determine a circle and, hence, a diameter D. A histogram of these diameter estimates is then used as a representation for objects (in the image) size.
Many video copy detection solutions use some spatial representation of frames or images (spatial representations as described above) and some temporal representation of the times between the frames, i.e., a spatial-temporal representation. Indyk et al. have proposed a method for video copy detection, solely using the distance (time) between shot breaks in the video as the feature of the video.                P. Indyk, G. Iyengar and N. Shivakumar, Finding pirated video sequences on the Internet. tech. rep., Stanford Infolab, February 1999.        
This method (incorporated herein by reference) is somewhat limited in the richness of the representation. Other video copy detection algorithms use some form of image matching (visual data) combined with temporal evidence integration. A method for detecting arbitrary video sequences, including commercials, is described in (incorporated herein by reference):                R. Mohan, “Video Sequence Matching”, International Conference on Acoustics, Speech and Signal Processing, (ICASSP), May 1998.        
Mohan defines that there is a match between a given video sequence and some segment of a database video sequence if each frame in the given sequence matches the corresponding frame in the database video segment. That is, the matching sequences are of the same temporal length; matching slow-motion sequences is performed by temporal sub-sampling of the database segments. The representation of a video segment is a vector of representations of the constituent frames in the form of an ordinal measure of a reduced intensity image of each frame. Before matching, the database is prepared for video sequence by computing the ordinal measure for each frame in each video segment in the database. Finding a match between some given action video sequence and the databases then amounts to sequentially matching the input sequence against each sub-sequence in the database and detecting minimums. This method introduces the temporal aspects of the video media items.
All these color-based image methods are subject to digitizing and encoding artifacts, like color variations. See A. Hampapur and R. M. Bolle, Feature based Indexing for Media Tracking. In Proc. of Int. Conf. on Multimedia and Expo, August 2000, pp. 67–70 (Hampapur et al.). To circumvent color variations Hampapur et al. have, instead, used other features that are invariant to color variations. In a first, off-line indexing phase representations for a set of known reference media items are computed and stored in an index structure. For each segment, a set of intervals is determined and from each key interval, a set of feature values is extracted from portions of the video frames. The values are quantized and index tables are built where feature values point to the reference media items. In the search and detection phase, a real-time process of computing and quantizing features from a target media stream is done in the same fashion. Additionally, counters are initialized for each of the known media items. When computed feature values point to a known media item, the corresponding counter is incremented. High values of the counter indicate the presence of a known media item in the target stream. An interesting thing to note here is that any feature type, such as, color, edges or motion, can be used in this method. Further, features are not computed on a frame basis (as in the above methods) but rather from regions within the frame and even regions of consecutive frames (local optical) flow. Detecting media items is further accomplished with a computational complexity that is sub-linear.
Reference Hampapur et al. is incorporated herein by reference.
What all these above mentioned references have in common is that the visual features extracted from the video do not have a whole lot of semantic meaning, e.g., a color, in and of itself, does not say much about the semantic content of the image or video. See Lienhart, C. Kuhmunch and W. Effelsberg, “On the detection and recognition of television commercials.” In Proc. of the IEEE Conf. on Multimedia Computing and Systems, 1997 (Lienhart et al.). Lienhart et al. take things a step further. They describe a system for performing both feature based detection and recognition of known commercials. The visual features that are used have spatial-temporal aspects. They use directly measurable features, such as, a spot being no longer than 30 seconds, spots being separated by a short break of 5–12 monochrome frames, and the volume of the audio signal being turned up. In addition, they use indirectly measurable features, like the fact that spots are full of motion, animated, and full of action. In addition, commercial spots have many still frames and many of them contain textual information. It is important to note that these are the beginnings of image processing techniques for extracting semantic information, such as action and motion, from video frames.
Reference Lienhart et al. is incorporated herein by reference.
Now consider B-T Truong, S. Venkatesh and C. Dorai, “Automatic Genre Identification for Content-Based Categorization,” in Proc. Int. Conf. On Pattern Recognition, September 2000, pp. 230–233 (B-T Truong et al.), incorporated herein in its entirety. The authors take the use of extracted semantic features a step further. The extracted visual features have cinematographic meaning, such as, fades, dissolves and motion features. Motion features are incorporated in terms of “quiet” visual scenes (the absence of motion) and “motion runs,” unbroken sequences of motion, where motion is defined in terms of luminance differences between frames. In addition, the authors use color features in terms of color coherence over time, high brightness and high saturation. The authors used the well-known C4.5 decision tree induction program to build a classifier for genre labeling.
Another technique for video categorization is described in                N. Dimitrova, L. Agnihotri and G. Wei, “Video classification based on HMM using text and faces,” (Dimitrova et al.).        
Herein, first fifteen labels defined based on these visual features (by text, the authors, mean superimposed text in the video) are defined, examples are “talking head” and “one text line.” A technique using Hidden Markov models (HMM) is described to classify a given media item into predefined categories, namely, commercial, news, sitcom and soap. An HMM takes these labels as input and has observation symbols as output. The system consists of two phases, a training and a classification stage. Reference Dimitrova et al. is incorporated herein in its entirety.
It is important to note that Dimitrova et al. does not use text in machine (ASCII) readable form, it uses the presence or absence of text block(s) in the video frames.
On the other hand, such machine-readable ASCII text, along with, visual features is used for video categorization in M. A. Smith and T. Kanade, “Video skimming for quick browsing based on audio and image characterization,” Carnegie Mellon University, Tech. Rep. CMU-CS-95-186, June 1995 (Smith et al.).
Reference Smith et al. is incorporated herein in its entirety. A sophisticated video database browsing systems is described, the authors refer to browsing as “skimming.” Much emphasis is placed on visual analysis for video interpretation and video summarization (the construction of two-dimensional depictions of the video to allow for nonlinear access). Visual analysis include scene break detection, camera motion analysis, and object detection (faces and superimposed text). The audio transcript is used to identify keywords in it. Term frequency inverse document frequency techniques are used to identify critical words. Words that appear frequently in a particular video segment but occur infrequently in standard corpuses receive the highest weight. In Smith et al. the speech recognition is not automated yet, and closed-captioning is used instead. Video search is accomplished through the use of the extracted words as search keys, browsing of video summaries then allows for quickly finding the video of interest.
A content-based video browsing system that applies linguistic analysis to the closed captioning is described in I. Mani, D. House, M. Maybury, M. Green, “Towards content-based browsing of broadcast news video,” in Intelligent Multimedia Info Retrieval, Issue 1997, M. T. Maybury (ed.), pp 241–258. AAAI Press/The MIT Press (Mani et al.).
The reference Mani et al. is incorporated herein in its entirety.
Emphasis in Mani et al. is placed on topic and story segmentation. Assuming that one could associate terms in a document with subjects in a thesaurus, the authors hypothesize that as topics change, the associated thesaural subjects change as well. The work is based on a thesaurus of 124 subject categories, with text summaries represented in a 124-dimensional space. Well-known subject similarity measures as the angle between subject vectors are used. The issue then is detecting a change in topic by detecting a change in angle. The subject vector, however, has to be computed over a certain video time interval, the authors refer to this as a block. The block size is important here. The authors do not arrive at a universally usable block size and contemplate adjustable block size. Further, the authors consider the use of cues that closed-captioners insert, in particular “>>” indicates a change of speaker, while “>>>” indicates a change in topic. These cues were found to be unreliable. Therefore, the authors investigate the use of what they call “sign off” cues. These are specific textual cues that indicate a change in topic, as “Goodnight Jim” in the MacNeil-Lehrer NewsHour shown in the past on PBS. The authors use no visual cues to detect story boundaries.
Finally, the use of automated speech recognition of the audio track to determine story and topic is being used more and more since speech recognition technology is steadily improving. The use of automated speech recognition can be classified as (1) dictation applications, (2) conversational or transactional applications, and (3) indexing applications. A comprehensive and excellent overview of the latter application is presented in Coden et al.:                A. R. Coden, E. W. Brown, “Speech Transcript Analysis for Automatic Search,” IBM Research Tech. Rep., IBM Research Tech. Rep., (Coden et al.).        
This reference (Coden et al.) is incorporated herein by reference. All of the video indexing, video summarization, video segmentation, and video categorization and subject detection technologies based on automated speech recognition, described in Coden et al., use no or very little visual information.
There is also quite some prior art dealing with segmenting documents (discourse) into portions corresponding to topics. This is typically referred to as “discourse segmentation” to distinguish it from character segmentation from image or video for optical character recognition (OCR). The term “discourse,” further is more general because it includes spoken language, which is transcribed from wave forms to text (e.g., ASCII) for analysis purposes. In the following discussion, we will use the terms interchangeably.
One popular recurring idea is to partition the discourse into fragments, and to measure the “similarity” of one fragment to another, using the cosine metric, which is the dot product of the word occurrence frequencies. (Morphological analysis is usually employed first, to reduce inflected, declined, etc., words to their base forms—“stemming” or “lemmatization”).
Reference Hearst, M. A., Multi-paragraph segmentation of expository text. Proceedings of the 32nd Annual Conference of the Association for Computational Linguistics, Las Cruces, N.Mex., 1994, pp. 9–16. (Hearst), is incorporated herein by reference.
Hearst does this by partitioning the entire document into tiles of more or less uniform size, the size being on the order of a paragraph. She then plots C (j, j+1) versus j, for j=1, . . . , N−1, where N is the number of tiles in the document, and C is the inter-tile co-occurrence (or similarity) coefficient. After smoothing of this curve, local minimal values indicate discourse boundaries, since minimal similarity indicates probable different topics of the tiles.
Also incorporated by reference is J. C. Reynar, “An automated method of finding topic boundaries,” Proceedings of the 32nd Annual Conference of the Association for Computational Linguistics, student session, Las Cruces, N.Mex., 1994, pp. 331–333, (Reynar). Reynar divides a discourse at a very fine grain: the individual word. He then records the correspondences (0 or 1) with every other word in an N×N matrix, where N is the document size in words. Then any choice of discourse boundaries defines a set of square sub-matrices of the matrix lying along the main diagonal, each sub-matrix representing the intra-segment co-occurrence values. Reynar defines the best discourse segmentation to be the one that minimizes the density of 1's in the extra-segmental co-occurrence regions of the matrix. Here the extra-segmental regions are all matrix entries not lying in the intra-segmental sub-matrices. He calls his technique dotplotting.
Further references Ponte and Croft, and Kozima are incorporated herein by reference:    Ponte J. M. and Croft W. B. 1997, Text Segmentation by Topic, in Proceedings of the First European Conference on Research and Advanced Technology for Digital Libraries, pp. 120–129. (Ponte and Croft)    Kozima, H. 1993 Text Segmentation based on similarity between words. In Proceedings of the 31st Annual Conference of the Association for Computational Linguistics, Columbus, Ohio. pp. 286–288, (Kozima)
Ponte and Croft, use a similar technique, except that they “expand” each word in a partition by looking it up in a “thesaurus” and taking all of the words in the same concept group that the seed word was in. (This is an attempt to overcome co-ocurrence, or correspondence, failures due to the use of synonyms or hypernyms, when really the same underlying concept is being referenced.) Ponte and Croft bootstrap the correspondences by developing a document-specific thesaurus, using “local context analysis” of labeled documents. Then, to find the best co-occurence sub-matrices, instead of exhaustively considering all possibilities, they use a dynamic programming technique, minimizing a cost function. Kozima et al. perform a similar word “expansion,” by means of “spreading activation” in a linguistic semantic net. Two words are considered to be co-occurrences of, or corresponding to, each other if and only if each can be reached from the other by less than m steps in the semantic net, for some arbitrarily chosen value of m.
There are feature-based approaches, too, that do not rely on word co-occurrence or correspondences, for example, Litman and Passoneau. Here a set of word features is developed. These features are derived from multiple knowledge sources: prosodic features, cue phrase features, noun phrase features, combined features. A decision tree, expressed in terms of these features, is then evaluated at each potential discourse segment boundary to decide if it is truly a discourse segmentation point or not. The decision expression can be hand-crafted or automatically produced by feeding training data to a learning system such as the well-known C4.5 decision tree classification scheme
Reference [Litman D. J. and Passoneau R. J. 1995. Combining multiple knowledge sources for discourse segmentation. In Proceedings of the 33rd Annual Conference of the Association for Computational Linguistics, Cambridge, Mass.], (Litman and Passoneau), is incorporated herein by reference.
Now consider [D. Beeferman, A. Berger and J. Lafferty, Text Segmentation Using Exponential Models, CMU Tech Rep], (Beeferman, et al.) that is incorporated herein by reference and introduces a feature-based discourse segmentation technique for documents. The idea is to assign to each position in the data stream a probability that a discourse boundary occurs. Central to the approach is a pair of tools: a short- and a long-range model of language. The short-term model is a trigram model, the conditional probability of a word based on the two preceding words. The long-term model is obtained by retaining a cache of recently seen trigrams. Determining a discourse boundary in statistical terms is cast by formulating the probability of a boundary both in terms of the short- and the long-term model. Maximal values of this probability then indicate discourse boundaries. Beeferman et al. touch upon, but do not implement, multimedia document (containing audio, text and video) discourse segmentation. Examples of short- long-term features that they propose are: “is there a sharp change in video stream in the last 20 frames,” “is there, a blank video frame nearby,” and “is the a match between the spectrum of the current image and the spectrum of the image near the last segment boundary.”
In sum, we can (roughly) distinguish the following approaches to media item categorization and media item subject detection; or, more generally, media item classification. The approaches are classified based on the features that are used. The features are derived from the raw analog signal, visual features computed from digitized media items frames (images), textual features directly decoded from the closed-caption, and textual features obtained from automatically computed speech transcripts. Here is a list of common kinds of features used to classify multimedia items:                Raw analog visual and audio signals.        Visual features computed from individual frames.        Visual features computed from individual frames plus temporal features.        Visual features computed from individual frames, temporal features plus audio features.        Semantic visual features computed from individual frames plus temporal features.        Semantic visual features computed from multiple frames and temporal features.        Closed-captioning (predetermined keyword spotting) plus visual features.        Speech transcript (predetermined keyword spotting) plus visual features.        Using only textual data, either speech transcript or closed-captioning.        Speech transcript computed from audio track.        Speech transcript computed from audio track plus rudimentary visual features.        Text document analysis.        